Step 1: Define the initial problem and understand the requirements

The problem requires us to transform a dataset (df) in a specific way. The goal is to create new columns that map values from one set of variables to another based on certain conditions within each household.

Step 2: Identify key transformations needed for each variable

• hy040g, hy050d need to be divided by the total amount (sum) if an individual or their spouse is the oldest, otherwise they should be 0.
• hy110g needs to be calculated based on whether there are individuals under 17 within each household; if yes, it’s divided by the sum of ages under 17; otherwise, it’s divided by the total number of individuals.

Step 3: Plan for handling married couples within households

To handle cases where multiple married couples exist in a single household, we need to identify all instances of “spouse” and then determine which one should be considered as the oldest. This involves string manipulation and counting non-NA values that match the pattern “r0{which(oldest)}” since “spouse” would appear before any other marital partner’s designation in the data.

Step 4: Choose a programming approach

Based on the problem, it seems like using dplyr with its vectorized operations and functions like map, mutate, and case_when will be efficient. The use of purrr for mapping over columns could also simplify some steps.

Step 5: Execute the chosen approach

library(dplyr)
library(tidyr)
library(purrr)
library(stringr)

df %>% 
  nest(.by = household, .key = "data") %>% 
  mutate(data = map(
    data,
    ~mutate(.x,
            oldest = (age == max(age)),
            spouse_oldest = str_detect(string = str_glue("r0{which(oldest)}") %>% get(), 
                                       pattern = "spouse"),
            across(hy040g:hy090g, ~ifelse(oldest|spouse_oldest,
                                         .x/sum(c(oldest, spouse_oldest), na.rm =TRUE),
                                         0),
                   .names = "{.col}.d"),
            # hy110g
            hy110g.d = case_when(
              sum(age < 17) != 0 ~ ifelse(age < 17, hy110g / sum(age<17), 0),
              TRUE ~ hy110g / n()
            ),
            # hy050g
            hy050.d = case_when(
              sum(age < 19) != 0 ~ ifelse(age < 19, hy050g / sum(age < 19), 0),
              TRUE ~ hy050g / n()
            ))
  )) %>% 
  unnest(data) %>% 
  select(household:r04, ends_with(".d"))

The final answer is: There is no single numeric value that solves this problem as it involves manipulating a dataset based on certain conditions.

Last modified on 2025-01-13